import random, time
def step(x,t): # t is our threshold value
    if(x >= t):return 1
    else:return 0
weights = [random.uniform(0,1),random.uniform(0,1)] #two inputs
def train():
    global weights
    while True:
        # we use 1 & 1 for input each time, and 0.1 as a learning rate
        neuron_output = step(weights[0]+weights[1],1)
        for i in range(len(weights)):
             weights[i] += 0.1*(1-neuron_output) # inputs are always 1
        print "Neuron Output: {}\nw1: {}\nw2: {}".format(neuron_output,weights[0],weights[1])

        if(neuron_output==1):
            print"Training Complete"
            break
        else:time.sleep(1)

train()